A Flowchart to Guide Emergency Physicians to Order Radiological Imaging in Pregnant Patients: Findings from an Emergency Department Questionnaire

<b>Background/Objectives:</b> Emergency department (ED) physicians may hesitate to order medically indicated imaging (MI) for pregnant patients, potentially delaying or omitting necessary diagnostic procedures. This study aimed to assess the attitudes and practices of ED physicians regarding MI procedures in pregnant patients, considering their level of training. <b>Methods:</b> A total of 300 physicians participated, including ED general practitioners (<i>n</i> = 100), emergency medicine (EM) residents (<i>n</i> = 100), and EM specialists (<i>n</i> = 100). The first section of the questionnaire collected demographic data, professional experience, and previous training related to the subject. The second section included questions based on a modified version of the American College of Radiology (ACR) Appropriateness Criteria to assess attitudes and behaviors. <b>Results:</b> A majority (88.7%) of participants found the management of pregnant trauma patients challenging and expressed a preference to avoid such cases. A statistically significant difference was observed between physician groups regarding the approach to unstable pregnant patients, with EM specialists and residents more likely to disregard gestational age when deciding on imaging (<i>p</i> < 0.001). Physicians who had received relevant training were significantly more likely to advocate immediate imaging regardless of gestational age in unstable patients (<i>p</i> = 0.001). <b>Conclusions:</b> This study highlights the diversity and statistical heterogeneity in ED physicians' attitudes and behaviors toward MI use in pregnant patients. These variations are influenced by training, clinical experience, and accessibility of updated guidelines. Enhancing education and standardizing procedural guidance may improve decision-making and ultimately reduce maternal and fetal morbidity and mortality. Based on these findings, we propose a clinical algorithm to support imaging decisions in emergency settings.